Syntax‐based metamorphic relation prediction via the bagging framework

2021 ◽  
Author(s):  
Yuyu Yin ◽  
Jiajie Ruan ◽  
Youhuizi Li ◽  
Yu Li ◽  
Zhijin Pan
Keyword(s):  
Relation Prediction

Reasoning over temporal knowledge graph with temporal consistency constraints

2021 ◽  
pp. 1-10
Author(s):  
Xiaojun Chen ◽  
Shengbin Jia ◽  
Ling Ding ◽  
Yang Xiang
Keyword(s):  
Short Term Memory ◽  
Knowledge Graph ◽  
Temporal Consistency ◽  
Short Term ◽  
Basic Model ◽  
Consistency Constraints ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Relation Prediction ◽  
Time Information

Knowledge graph reasoning or completion aims at inferring missing facts by reasoning about the information already present in the knowledge graph. In this work, we explore the problem of temporal knowledge graph reasoning that performs inference on the graph over time. Most existing reasoning models ignore the time information when learning entities and relations representations. For example, the fact (Scarlett Johansson, spouse Of, Ryan Reynolds) was true only during 2008 - 2011. To facilitate temporal reasoning, we present TA-TransRILP, which involves temporal information by utilizing RNNs and takes advantage of Integer Linear Programming. Specifically, we utilize a character-level long short-term memory network to encode relations with sequences of temporal tokens, and combine it with common reasoning model. To achieve more accurate reasoning, we further deploy temporal consistency constraints to basic model, which can help in assessing the validity of a fact better. We conduct entity prediction and relation prediction on YAGO11k and Wikidata12k datasets. Experimental results demonstrate that TA-TransRILP can make more accurate predictions by taking time information and temporal consistency constraints into account, and outperforms existing methods with a significant improvement about 6-8% on Hits@10.

A Hybrid Method Based on Semi-Supervised Learning for Relation Extraction in Chinese EMRs (Preprint)

2021 ◽  
Author(s):  
ChunMing Yang
Keyword(s):  
Supervised Learning ◽  
Learning Algorithm ◽  
Medical Knowledge ◽  
Relation Extraction ◽  
Small Scale ◽  
Semantic Features ◽  
Training Process ◽  
Network Layers ◽  
Relation Prediction ◽  
The Cost

BACKGROUND Extracting relations between the entities from Chinese electronic medical records(EMRs) is the key to automatically constructing medical knowledge graphs. Due to the less available labeled corpus, most of the current researches are based on shallow networks, which cannot fully capture the complex semantic features in the text of Chinese EMRs. OBJECTIVE In this study, a hybrid deep learning method based on semi-supervised learning is proposed to extract the entity relations from small-scale complex Chinese EMRs. METHODS The semantic features of sentences are extracted by residual network (ResNet) and the long dependent information is captured by bidirectional GRU (Gated Recurrent Unit). Then the attention mechanism is used to assign weights to the extracted features respectively, and the output of the two attention mechanisms is integrated for relation prediction. We adjusted the training process with manually annotated small-scale relational corpus and bootstrapping semi-supervised learning algorithm, and continuously expanded the datasets during the training process. RESULTS The experimental results show that the best F1-score of the proposed method on the overall relation categories reaches 89.78%, which is 13.07% higher than the baseline CNN model. The F1-score on DAP, SAP, SNAP, TeRD, TeAP, TeCP, TeRS, TeAS, TrAD, TrRD and TrAP 11 relation categories reaches 80.95%, 93.91%, 92.96%, 88.43%, 86.54%, 85.58%, 87.96%, 94.74%, 93.01%, 87.58% and 95.48%, respectively. CONCLUSIONS The hybrid neural network method strengthens the feature transfer and reuse between different network layers and reduces the cost of manual tagging relations. The results demonstrate that our proposed method is effective for the relation extraction in Chinese EMRs.

scholarly journals Knowledge Graph Completion for the Chinese Text of Cultural Relics Based on Bidirectional Encoder Representations from Transformers with Entity-Type Information

Entropy ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. 1168
Author(s):  
Min Zhang ◽  
Guohua Geng ◽  
Sheng Zeng ◽  
Huaping Jia
Keyword(s):  
Chinese Text ◽  
Large Scale ◽  
Semantic Information ◽  
Training Model ◽  
Knowledge Graph ◽  
Deep Model ◽  
The Rich ◽  
Relation Prediction ◽  
Type Information ◽  
Cultural Relics

Knowledge graph completion can make knowledge graphs more complete, which is a meaningful research topic. However, the existing methods do not make full use of entity semantic information. Another challenge is that a deep model requires large-scale manually labelled data, which greatly increases manual labour. In order to alleviate the scarcity of labelled data in the field of cultural relics and capture the rich semantic information of entities, this paper proposes a model based on the Bidirectional Encoder Representations from Transformers (BERT) with entity-type information for the knowledge graph completion of the Chinese texts of cultural relics. In this work, the knowledge graph completion task is treated as a classification task, while the entities, relations and entity-type information are integrated as a textual sequence, and the Chinese characters are used as a token unit in which input representation is constructed by summing token, segment and position embeddings. A small number of labelled data are used to pre-train the model, and then, a large number of unlabelled data are used to fine-tune the pre-training model. The experiment results show that the BERT-KGC model with entity-type information can enrich the semantics information of the entities to reduce the degree of ambiguity of the entities and relations to some degree and achieve more effective performance than the baselines in triple classification, link prediction and relation prediction tasks using 35% of the labelled data of cultural relics.

scholarly journals Rationalizing Medical Relation Prediction from Corpus-level Statistics

2020 ◽  
Author(s):  
Zhen Wang ◽  
Jennifer Lee ◽  
Simon Lin ◽  
Huan Sun
Keyword(s):  
Level Statistics ◽  
Relation Prediction

scholarly journals Variational Deep Logic Network for Joint Inference of Entities and Relations

2021 ◽  
pp. 1-38
Author(s):  
Wenya Wang ◽  
Sinno Jialin Pan
Keyword(s):  
Deep Learning ◽  
Representation Learning ◽  
Event Extraction ◽  
Relational Reasoning ◽  
Entity Extraction ◽  
Learning Models ◽  
Joint Inference ◽  
Logic Network ◽  
End To End ◽  
Relation Prediction

Abstract Nowadays, deep learning models have been widely adopted and achieved promising results on various application domains. Despite of their intriguing performance, most deep learning models function as black-boxes, lacking explicit reasoning capabilities and explanations, which are usually essential for complex problems. Take joint inference in information extraction as an example. This task requires the identification of multiple structured knowledge from texts, which is inter-correlated, including entities, events and the relationships between them. Various deep neural networks have been proposed to jointly perform entity extraction and relation prediction, which only propagate information implicitly via representation learning. However, they fail to encode the intensive correlations between entity types and relations to enforce their co-existence. On the other hand, some approaches adopt rules to explicitly constrain certain relational facts. However, the separation of rules with representation learning usually restrains the approaches with error propagation. Moreover, the pre-defined rules are inflexible and might bring negative effects when data is noisy. To address these limitations, we propose a variational deep logic network that incorporates both representation learning and relational reasoning via the variational EM algorithm. The model consists of a deep neural network to learn high-level features with implicit interactions via the self-attention mechanism and a relational logic network to explicitly exploit target interactions. These two components are trained interactively to bring the best of both worlds. We conduct extensive experiments ranging from fine-grained sentiment terms extraction, end-to-end relation prediction to end-to-end event extraction to demonstrate the effectiveness of our proposed method.

scholarly journals Distant Supervision for Relation Extraction with Sentence Selection and Interaction Representation

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Tiantian Chen ◽  
Nianbin Wang ◽  
Hongbin Wang ◽  
Haomin Zhan
Keyword(s):  
Large Scale ◽  
Semantic Information ◽  
State Of The Art ◽  
Relation Extraction ◽  
Semantic Features ◽  
Distant Supervision ◽  
Word Level ◽  
Proposed Model ◽  
Relation Prediction ◽  
Better Than

Distant supervision (DS) has been widely used for relation extraction (RE), which automatically generates large-scale labeled data. However, there is a wrong labeling problem, which affects the performance of RE. Besides, the existing method suffers from the lack of useful semantic features for some positive training instances. To address the above problems, we propose a novel RE model with sentence selection and interaction representation for distantly supervised RE. First, we propose a pattern method based on the relation trigger words as a sentence selector to filter out noisy sentences to alleviate the wrong labeling problem. After clean instances are obtained, we propose the interaction representation using the word-level attention mechanism-based entity pairs to dynamically increase the weights of the words related to entity pairs, which can provide more useful semantic information for relation prediction. The proposed model outperforms the strongest baseline by 2.61 in F1-score on a widely used dataset, which proves that our model performs significantly better than the state-of-the-art RE systems.

scholarly journals Relation prediction in knowledge graph by Multi-Label Deep Neural Network

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Yohei Onuki ◽  
Tsuyoshi Murata ◽  
Shun Nukui ◽  
Seiya Inagi ◽  
Xule Qiu ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Knowledge Graph ◽  
Relation Prediction
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